AttentionCovidNet: Efficient ECG-based diagnosis of COVID-19

Wesley Chorney; Haifeng Wang; Lir-Wan Fan

doi:10.1016/j.compbiomed.2023.107743

AttentionCovidNet: Efficient ECG-based diagnosis of COVID-19

Comput Biol Med. 2024 Jan:168:107743. doi: 10.1016/j.compbiomed.2023.107743. Epub 2023 Nov 22.

Authors

Wesley Chorney¹, Haifeng Wang², Lir-Wan Fan³

Affiliations

¹ Computational Engineering, Mississippi State University, Mississippi State, 39762, USA. Electronic address: wwc102@msstate.edu.
² Industrial and Systems Engineering, Mississippi State University, Mississippi State, 39762, USA. Electronic address: wang@ise.msstate.edu.
³ Pediatrics/Newborn Medicine, University of Mississippi Medical Center, Mississippi State, 39216, USA. Electronic address: lwfan@umc.edu.

PMID: 38000247
DOI: 10.1016/j.compbiomed.2023.107743

Abstract

The novel coronavirus caused a worldwide pandemic. Rapid detection of COVID-19 can help reduce the spread of the novel coronavirus as well as the burden on healthcare systems worldwide. The current method of detecting COVID-19 suffers from low sensitivity, with estimates of 50%-70% in clinical settings. Therefore, in this study, we propose AttentionCovidNet, an efficient model for the detection of COVID-19 based on a channel attention convolutional neural network for electrocardiograms. The electrocardiogram is a non-invasive test, and so can be more easily obtained from a patient. We show that the proposed model achieves state-of-the-art results compared to recent models in the field, achieving metrics of 0.993, 0.997, 0.993, and 0.995 for accuracy, precision, recall, and F1 score, respectively. These results indicate both the promise of the proposed model as an alternative test for COVID-19, as well as the potential of ECG data as a diagnostic tool for COVID-19.

Keywords: COVID-19; Channel attention; Diagnosis; Electrocardiograms.

MeSH terms

Benchmarking
COVID-19 Testing
COVID-19* / diagnosis
Electrocardiography
Humans
Neural Networks, Computer
SARS-CoV-2